Satellite images enable the analysis of the spatial and temporal distribution of burned areas (BA) and burn severity (BS) to quantify the impacts of wildfires. However, the generation of reliable maps requires algorithms and tools available to the user for regional to global analyses. We present a public QGIS plugin (BAD, Burned Area Detector) for automatic BA and BS mapping using pre- and post-fire Sentinel-2 multispectral images. The plugin also incorporates a validation module for assessing the accuracy of output maps. The BA detection is based on a multi-criteria soft computing approach that incorporates experts’ fuzzy knowledge and its integration, combined with a region growing algorithm. BS is estimated using the difference of the Normalized Burn Ratio (NBR) index.The plugin was tested on wildfire events in Spain (summer 2022) and California (winter 2025). Besides proving the functionalities of BAD, these test cases confirm the robustness of the algorithm when applied automatically to Mediterranean regions and its flexibility in ingesting different data sources (active fires as seeds for the region growing). Results across all studied areas show an average omission error of 9.57%, a commission error of 16.56%, and a Dice coefficient of 89.62%.
Martinoli et al. (2026) studied this question.